AQUAINT 18-Month Workshop 1 Light Semantic Processing for QA Language Technologies Institute, Carnegie Mellon B. Van Durme, Y. Huang, A. Kupsc and E. Nyberg Towards Light Semantic Processing for Question Answering

AQUAINT 18-Month Workshop 2 Light Semantic Processing for QA Overview of This Talk Motivation Components of the Approach –Logical Form –Similarity Measure –Unification Strategy Incorporation into JAVELIN Future Work / Next Steps

AQUAINT 18-Month Workshop 3 Light Semantic Processing for QA Example of Extraction Error Question: “When was Wendy’s founded?” Passage candidate: –“The renowned Murano glassmaking industry, on an island in the Venetian lagoon, has gone through several reincarnations since it was founded in Three exhibitions of 20th-century Murano glass are coming up in New York. By Wendy Moonan.” Statistical extractor: 20th-century

AQUAINT 18-Month Workshop 4 Light Semantic Processing for QA Basic Idea Q: “xxx xxxx xxxx xxxx xxxxxxxxxx xx xxxxx?”P: “xxx xxxx xxxx xxxx xxxxx xx xxxxx.” A(?,C)A(B,C)A(B,C) ? = B extract Unification on simple predicates representing basic argument structure will provide a more accurate way to match questions with appropriate answer(s) Two Challenges: * Where do predicates come from? * Flexibility in interpretation… partial interpretation

AQUAINT 18-Month Workshop 5 Light Semantic Processing for QA Associating Tokens with Concepts Imprecise Reference, e.g.: “John W. was greeted by William Clinton” “Bill greeted Mr. Wright” Definite Description, e.g. “Mr. Bush” vs. “the president” Anaphoric Reference UNIFY( {GREET(“William Clinton”,”John W.”)}, {GREET(“Bill”,”Mr. Wright”)} ) Interpretation of tokens must be: Approximate, not exact Context-sensitive

AQUAINT 18-Month Workshop 6 Light Semantic Processing for QA Language Processing Tools BBN IdentiFinder (BBN, 2000) Link Grammar parser (Grinberg et al., 1995) KANTOO parser (Nyberg & Mitamura, 2000) Brill part-of-speech tagger (Brill, 1995) WordNet (Fellbaum, 1998) Lexical Conceptual Structure (LCS) Database (Dorr 2001)

AQUAINT 18-Month Workshop 7 Light Semantic Processing for QA Representation Formula: a set of literals Literal: a predicate, plus two terms Extrinsic literal: a relation mapping a label to a label –SUBJECT(x1,x2) Intrinsic literal: a relation mapping a label to a value –ROOT(x1,|Benjamin|) Value: EVENT, past, +, |Mary Smith|,…

AQUAINT 18-Month Workshop 8 Light Semantic Processing for QA Example Q = Who killed Jefferson? ROOT(x1,?a0),ROOT(x2,|kill|),ROOT(x3,|Jefferson|), TYPE(x2,|event|),TYPE(x1,|person|),TYPE(x3,|person|), SUBJECT(x2,x1),OBJECT(x2,x3),ANS(?a0) P = Benjamin murdered Jefferson. ROOT(y1,|Benjamin|),ROOT(y2,|murder|),ROOT(y3,|Jef ferson|), TYPE(y2,|event|),TYPE(y1,|person|),TYPE(y3,|person|), SUBJECT(y2,y1),OBJECT(y2,y3)

AQUAINT 18-Month Workshop 9 Light Semantic Processing for QA Graphically ?a0 x1 x2 kill x3 Jefferson person Benjamin y1 y2 murder y3 Jefferson person event person event SUBJECT OBJECT ROOT TYPE

AQUAINT 18-Month Workshop 10 Light Semantic Processing for QA Similarity Functions A zero-to-one function that returns a value representing similarity between the formulae for question, passage Unification requires similarity measurement between literal values sim(“Who killed Jefferson?”, ”Benjamin murdered Jefferson.”) = 0.9

AQUAINT 18-Month Workshop 11 Light Semantic Processing for QA sim(formula0,formula1) Given two formulae, we define the similarity to be the geometric mean of the similarity between the separate extrinsic literals.

AQUAINT 18-Month Workshop 12 Light Semantic Processing for QA sim(extrinsicLiteral0,extrinsicLiteral1) To measure the similarity between two extrinsic literals, we take the square root of the product of the similarity between each of the two pairs of labels.

AQUAINT 18-Month Workshop 13 Light Semantic Processing for QA sim(label0,label1) To measure the similarity of two labels, we find the maximum possible value of taking the geometric mean of the similarity of each pairwise combination of intrinsic literals that are shared by the two labels.

AQUAINT 18-Month Workshop 14 Light Semantic Processing for QA sim(intrinsicLiteral0,intrinsicLiteral1) The similarity between two intrinsic literals is measured by similarity of the paired words, times the weight of the first literal.

AQUAINT 18-Month Workshop 15 Light Semantic Processing for QA sim(word0,word1) sim(|kill|,|murder|) = 0.8 –via WordNet distance function sim(?a0,|Benjamin|) = 1.0 –zero cost for variable binding

AQUAINT 18-Month Workshop 16 Light Semantic Processing for QA Example

AQUAINT 18-Month Workshop 17 Light Semantic Processing for QA Answer Find the maximum possible similarity score, return the term bound to ?a0 ?a0/|Benjamin| sim(Q,P) = 0.9 Answer = Benjamin, 0.9

AQUAINT 18-Month Workshop 18 Light Semantic Processing for QA Current Status, Future Work First version implemented, testing now Short Term: Test “NLP IX” against statistical extraction module on factoid questions Longer Term: –Support simple reasoning about questions and passages –Investigate approach in narrower domains Question answering based on CNS data on terrorism and weapons of mass destruction –Extend similarity metric at word level Word co-occurrence information Distance metrics on ontologies other than WordNet –Incorporate LCS Lexicon

AQUAINT 18-Month Workshop 19 Light Semantic Processing for QA Summary We believe complex question answering requires more than statistical extraction methods Knowledge bottleneck forces compromise in depth of language processing Robust unification based on heuristic measure of similarity offers short-term solution

AQUAINT 18-Month Workshop 20 Light Semantic Processing for QA Additional Resources Paper available: B. Van Durme, Y. Huang, A. Kupsc and E. Nyberg (2003). “Towards Light Semantic Processing for Question Answering”, presented at the HLT/NAACL 2003 Workshop on Text Meaning. This and other papers at the JAVELIN web site:

AQUAINT 18-Month Workshop 21 Light Semantic Processing for QA Questions?

AQUAINT 18-Month Workshop 22 Light Semantic Processing for QA Logical Form := + := (, ) := | := |[a-nA-Z0-9\s]+| := [a-z]+[0-9]+ := [A-Z]+ Extrinsic literal: (, ) Intrinsic literal: (, )